op {
  graph_op_name: "UniformDequantize"
  visibility: HIDDEN
  in_arg {
    name: "input"
    description: <<END
Must be a Tensor of Tin.
END
  }
  in_arg {
    name: "scales"
    description: <<END
The float value(s) used as scale(s) when quantizing original data that input represents.
Must be a scalar Tensor if quantization_axis is -1 (per-tensor quantization), otherwise 1D Tensor of size (input.dim_size(quantization_axis),) (per-axis quantization).
END
  }
  in_arg {
    name: "zero_points"
    description: <<END
The int32 value(s) used as zero_point(s) when quantizing original data that input represents.
Same shape condition as scales.
END
  }
  out_arg {
    name: "output"
    description: <<END
The output dequantized Tensor of Tout, whose shape is same as input.
END
  }
  attr {
    name: "Tin"
    description: <<END
The type of input Tensor. A tf.DType from: tf.float32
END
  }
  attr {
    name: "Tout"
    description: <<END
The type of output Tensor. A tf.DType from: tf.qint8, tf.qint32
END
  }
  attr {
    name: "quantization_axis"
    description: <<END
Indicates the dimension index of the tensor where per-axis quantization is applied for the slices along that dimension.
If set to -1 (default), this indicates per-tensor quantization. Otherwise, it must be set within range [0, input.dims()).
END
  }
  attr {
    name: "quantization_min_val"
    description: <<END
The quantization min value that was used when input was quantized.
The purpose of this attribute is typically (but not limited to) to indicate narrow range, where this is set to:
`(Tin lowest) + 1` if narrow range, and `(Tin lowest)` otherwise.
For example, if Tin is qint8, this is set to -127 if narrow range quantized or -128 if not.
END
  }
  attr {
    name: "quantization_max_val"
    description: <<END
The quantization max value that was used when input was quantized.
The purpose of this attribute is typically (but not limited to) indicate narrow range, where this is set to:
`(Tout max)` for both narrow range and not narrow range.
For example, if Tin is qint8, this is set to 127.
END
  }
  summary: "Perform dequantization on the quantized Tensor `input`."
  description: <<END
Given quantized `input` which was quantized using `scales` and `zero_points`, performs dequantization using the formula:
dequantized_data = (quantized_data - zero_point) * scale.
END
}
